Computer modelling using cellular automata of the survival fraction of cell populations under irradiation Morgiane Richard Examples of glioma cells T98g and U373 and hamster fibroblast cells V79 27-01-2005 Krakow

Table of contents People and places involved. Organisation and justification of the project. Biological background of the project. Relevance of the chosen model, cellular automata.

Actors of the project Miss Morgiane Richard Supervisors: Dr. N.F. Kirkby, University of Surrey Prof. R.P. Webb, University of Surrey Dr. K.J. Kirkby, University of Surrey

Location of the project University of Surrey, Guildford, Great Britain Guildford

How will the project unfold Computer modelling of cell behaviour under irradiation Experimental validation of models at the Gray Cancer Institute, Cambridge or at the University of Surrey, Ion Beam Center.

A big issue behind Radiotherapy has been widely used for curing cancer. Aim of radiation: to kill cancer cells, and destroy the tumour, without affecting healthy tissues. Results in radiotherapy can still be optimised.

Effect of radiation on cells Biological processes following cells irradiation: DNA damage: Double strand breaks Single strand breaks Irradiation: X-ray, γ Cell Repair processes: Base Excision Repair Homologous Recombination Non Homologous End Joining

Effect of radiation on cells Two effects have been discovered Low-Dose Hyper Radiosensitivity (LDHRS), coupled with Increased Radioresistance (IRR). The bystander effect.

Effect of radiation on cells Illustration of hyper radiosensitivity

Effect of radiation on cells LDHRS/IRR not fully understood yet Irradiation effects: Depend on intrinsic characteristic of cells. Trigger intra-cellular signals.

Effect of radiation on cells Bystander effects: non-irradiated cells are affected. Irradiation effects: Involve non-irradiated cells. Spread through inter-cellular signals Petri dish

Cellular automata (CA) Definition: Network of cells at an initial state, Finite set of rules, A neighbourhood: a concentration field (signals…). NB: a CA cell is NOT always a biological cell!

Cellular automata Time evolution: State(t+1) depends on state(t). Changes according to the rules and the neighbourhood.

Cellular automata Example of 1D CA: Infinite line of cells, all of them at 0 and one at 1. Neighbourhood: two neighbour cells. Rule: ct+1(i)= ct(i+1)+ct(i-1) mod 2

Cellular automata 1 Example of 1D CA: Time evolution of the CA: T = 0 1 T = 0 T = 1 T = 2 T = 3

Cellular automata for biological cells 11 states: G0, G1, S, G2, M +/- DNA damage and D? Simple transitions: S->M and M->2G1 Check points G1->S and G2->M Effect of radiation in each state? Transmission and reception of signals?

Cellular automata Conclusion: possibility of modelling Intrinsic effects of radiation Bystander effects of radiation

Questions?

Simple cell model

Survival curve of a population

Population model against experimental data Exp. data Short et al, GCI